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Systems Engineering Assignment #6 Solution


1. [5 points] Consider the following training set




x 1 =
0
,t 1 = 0  ,  x 2 =
0
,t 2 = 1 ,  x 3 =
1
,t 3 = 1 ,  x 4 =
1
,t 4 = 1










0

1

0

1

a) Plot the training samples in the feature space.










    b) Apply the perceptron learning rule to the training samples one-at-a-time to obtain weights w1, w2, and bias w0 that separate the training samples. Use w = [w0, w1, w2] = [0, 0, 0] as initial values (consider bias input x0 = 1, and learning rate = 1). Write the expression for the resulting decision boundary and draw it in the graph. [Hint: You can use Excel / OO Calc to implement the learning rule for perceptron, such as the spreadsheet of InClass_09 posted on eClass].

Epoch
Inputs
Desired
Initial weights
Actual
Error
Updated weights



output t



output y





x1
x2

w0
w1
w2


w0
w1
w2
1
0
0
0
0
0
0






0
1
1









1
0
1









1
1
1








2
0
0
0





















0
1
1









1
0
1









1
1
1








3
0
0
0









0
1
1









1
0
1









1
1
1





















2. [5 points] Consider the following training set





x 1 =
0
,t 1 = 0  ,  x 2 =
0
,t 2
= 1 ,  x 3 =
1
,t 3 = 1 ,  x 4 =
1
,t 4 = 0











0

1


0

1

which describes the exclusive OR (XOR) problem.

    a) Establish mathematical (not graphical) proof that this problem is not linearly separable. [Hint: Start with assumption that these patterns are linearly separable, write down equations/inequalities corresponding to this assumption and examine them for conflict; first such inequality is provided below as an example.]

Suppose that the problem is linearly separable. The decision boundary can be represented as:

∑20 = 0 or (expanded) 0 0 + 1 1 + 2 2 = 0 This assumption means that either
+    +    < 0       (   ,    ) = (0,1) ∧ (   ,    ) = (1,0)
    a) 0  0 + 1  1 + 2  2 ≥ 0       (  1, 2) = (0,0) ∧ (  1, 2) = (1,1),

or  0  0    1  1    2  21  21  2
+    +    > 0       (   ,    ) = (0,1) ∧ (   ,    ) = (1,0)
    b) 0  0+ 1 1+ 2  2 ≤0      (  1, 2)=(0,0)∧(  1, 2)=(1,1).0011221212
must be satisfied. Following one of the cases and putting the values (  1,    2) under variables, one obtains
    (1) 0 0+ 2<0

(2)

(3)

(4)
















    b) Apply the perceptron learning rule following the same procedure as in Problem 1. Describe your observation.
Epoch
Inputs
Desired
Initial weights
Actual
Error
Updated weights



output t



output y





x1
x2

w0
w1
w2


w0
w1
w2
1
0
0
0
0
0
0






0
1
1









1
0
1









1
1
0








2
0
0
0









0
1
1









1
0
1









1
1
0








3
0
0
0









0
1
1









1
0
1









1
1
0








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